Climate change research goes to the extremes

By now, most sci­en­tists—97% of them, to be exact—agree that the tem­per­a­ture of the planet is rising and that the increase is due to human activ­i­ties such as fossil fuel use and defor­esta­tion. But until recently, the jury was still out regarding the vari­ability sur­rounding that increase—for example, how much dif­fer­ence there will be between the hottest hot days from one year to the next, as well as with each year’s coldest cold days.

Some studies sug­gested an increase in vari­ability, others a decrease. The problem with these results, said Evan Kodra, PhD’14, is that none of them took a sys­tem­atic approach to gleaning that answer. Each was exam­ining some other phenomenon—such as whether a par­tic­ular region would expe­ri­ence overall warming—and the vari­ability data was a sec­ondary, but inter­esting, finding.

That’s why Kodra and his adviser Auroop Gan­guly, a cli­mate change expert and asso­ciate pro­fessor in Northeastern’s Depart­ment of Civil and Envi­ron­mental Engi­neering, decided to take a dif­ferent approach in their paper pub­lished online on Wednesday in the journal Sci­en­tific Reports, pub­lished by Nature. Their work was per­formed in Northeastern’s Sus­tain­ability and Data Sci­ences Lab­o­ra­tory run by Ganguly.

What they found may sur­prise some: While global tem­per­a­ture is indeed increasing, so too is the vari­ability in tem­per­a­ture extremes. For instance, while each year’s average hottest and coldest tem­per­a­tures will likely rise, those aver­ages will also tend to fall within a wider range of poten­tial high and low tem­perate extremes than are cur­rently being observed.

This means that even as overall tem­per­a­tures rise, we may still con­tinue to expe­ri­ence extreme cold snaps, said Kodra, who earned the Col­lege of Engineering’s out­standing grad­uate research award in 2014 and is now leading data ana­lytics efforts at Energy Points, an inno­v­a­tive Boston area startup.

That is an impor­tant point in the ongoing effort to accu­rately inform the public about cli­mate change. “Just because you have a year that’s colder than the usual over the last decade isn’t a rejec­tion of the global warming hypoth­esis,” Kodra explained.

The new results pro­vide impor­tant sci­en­tific as well as soci­etal impli­ca­tions, Gan­guly noted. For one thing, knowing that models project a wider range of extreme tem­per­a­ture behavior will allow sec­tors like agri­cul­ture, public health, and insur­ance plan­ning to better pre­pare for the future. For example, Kodra said, “an agri­cul­ture insur­ance com­pany wants to know next year what is the coldest snap we could see and hedge against that. So, if the range gets wider they have a broader array of poli­cies to consider.”

With funding from a $10-​​million multi-​​university Expe­di­tions in Com­puting grant on under­standing cli­mate change, the duo used com­pu­ta­tional tools from Big Data sci­ence to sys­tem­at­i­cally examine this aspect of cli­mate change for the first time. This study did just that, bringing together a unique com­bi­na­tion of com­pu­ta­tional data sci­ence tools tai­lored for extracting nuanced insights about cli­mate extremes.

The research also opens new areas of interest for future work, both in cli­mate and data sci­ence. It sug­gests that the nat­ural processes that drive weather anom­alies today could con­tinue to do so in a warming future. For instance, the team spec­u­lates that ice melt in hotter years may cause colder sub­se­quent win­ters, but these hypotheses can only be con­firmed in physics-​​based studies.

The study used sim­u­la­tions from the most recent cli­mate models devel­oped by groups around the world for the Inter­gov­ern­mental Panel on Cli­mate Change and “reanalysis data sets,” which are gen­er­ated by blending the best avail­able weather obser­va­tions with numer­ical weather models. The team com­bined a suite of methods in a rel­a­tively new way to char­ac­terize extremes and explain how their vari­ability is influ­enced by things like the sea­sons, geo­graph­ical region, and the land-​​sea inter­face. The analysis of mul­tiple cli­mate model runs and reanalysis data sets was nec­es­sary to account for uncer­tain­ties in the physics and model imperfections.